Recent work have alleviated this problem by using context information[1]}, [2]}, progressive refinement[3]}, [4]}, [5]}, and smoothed loss functions[5]}. For example, Contextlocnet[1]} built additive and contrastive guidance models to utilize their surrounding context feature to enhance localization performance. C-MIL[5]} divided the instances in the bag into multiple subsets, and defines corresponding smoother loss functions on the each subset to approximate the convex loss function.
